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Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.
Muhammad Rasool Al-Kilani; Michel Rahbeh; Jawad Al-Bakri; Tsegaye Tadesse; Cody Knutson. Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan. Earth Systems and Environment 2021, 1 -13.
AMA StyleMuhammad Rasool Al-Kilani, Michel Rahbeh, Jawad Al-Bakri, Tsegaye Tadesse, Cody Knutson. Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan. Earth Systems and Environment. 2021; ():1-13.
Chicago/Turabian StyleMuhammad Rasool Al-Kilani; Michel Rahbeh; Jawad Al-Bakri; Tsegaye Tadesse; Cody Knutson. 2021. "Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan." Earth Systems and Environment , no. : 1-13.
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
Trenton Benedict; Jesslyn Brown; Stephen Boyte; Daniel Howard; Brian Fuchs; Brian Wardlow; Tsegaye Tadesse; Kirk Evenson. Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions. Remote Sensing 2021, 13, 1210 .
AMA StyleTrenton Benedict, Jesslyn Brown, Stephen Boyte, Daniel Howard, Brian Fuchs, Brian Wardlow, Tsegaye Tadesse, Kirk Evenson. Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions. Remote Sensing. 2021; 13 (6):1210.
Chicago/Turabian StyleTrenton Benedict; Jesslyn Brown; Stephen Boyte; Daniel Howard; Brian Fuchs; Brian Wardlow; Tsegaye Tadesse; Kirk Evenson. 2021. "Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions." Remote Sensing 13, no. 6: 1210.
Vegetation growth plays a crucial role in the carbon cycle and climate change mitigation. However, the relative contribution of hydroclimatic variables (relative humidity, terrestrial water storage, day and night‐time land surface temperatures) on vegetation growth of agricultural and non‐agricultural areas at the global scale remains unexplored. Using satellite‐based datasets, we examined the changes in Normalized Difference Vegetation Index (NDVI) and the four hydroclimatic variables during 2003‐2014. Also, the relative contribution of the four hydroclimatic variables on vegetation growth in agricultural and non‐agricultural areas was estimated. A significant (p‐value < 0.05) greening has occurred in the agricultural regions of India and Brazil during 2003‐2014. Whereas in non‐agriculture areas, a considerable greening occurred only in India and China during the 2003‐2014 period. Among the four hydroclimatic variables, both day‐time and night‐time land surface temperature are the significant contributors of vegetation growth in the two‐thirds of the global landmass. Terrestrial water storage is a substantial contributor to the vegetation growth in the tropics and sub‐tropics. Night‐time land surface temperature is strongly associated with the vegetation growth in the colder regions. The hydroclimatic variables do not explain the considerable amount of the total variance of vegetation growth over the agricultural areas in China, which is due to human agricultural management practices. Generally, the response of hydroclimate variables on vegetation growth in the agricultural and non‐agricultural areas has significant implications in many areas, including food security, carbon sequestration, water resource management, and climate change. This article is protected by copyright. All rights reserved.
Akarsh Asoka; Brian Wardlow; Tadesse Tsegaye; Matthew Huber; Vimal Mishra. A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .
AMA StyleAkarsh Asoka, Brian Wardlow, Tadesse Tsegaye, Matthew Huber, Vimal Mishra. A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions. Journal of Geophysical Research: Atmospheres. 2021; 126 (5):1.
Chicago/Turabian StyleAkarsh Asoka; Brian Wardlow; Tadesse Tsegaye; Matthew Huber; Vimal Mishra. 2021. "A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions." Journal of Geophysical Research: Atmospheres 126, no. 5: 1.
The term “flash drought” is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that “flash drought” is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon.
Mahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. Flash drought onset over the contiguous United States: sensitivity of inventories and trends to quantitative definitions. Hydrology and Earth System Sciences 2021, 25, 565 -581.
AMA StyleMahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, Martha C. Anderson. Flash drought onset over the contiguous United States: sensitivity of inventories and trends to quantitative definitions. Hydrology and Earth System Sciences. 2021; 25 (2):565-581.
Chicago/Turabian StyleMahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. 2021. "Flash drought onset over the contiguous United States: sensitivity of inventories and trends to quantitative definitions." Hydrology and Earth System Sciences 25, no. 2: 565-581.
Monitoring drought impacts in forest ecosystems is a complex process because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.
Tsegaye Tadesse; David Hollinger; Yared Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian Wardlow; Gil Bohrer; Kenneth Clark; Ankur Desai; Lianhong Gu; Asko Noormets; Kimberly Novick; Andrew Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. Remote Sensing 2020, 12, 3605 .
AMA StyleTsegaye Tadesse, David Hollinger, Yared Bayissa, Mark Svoboda, Brian Fuchs, Beichen Zhang, Getachew Demissie, Brian Wardlow, Gil Bohrer, Kenneth Clark, Ankur Desai, Lianhong Gu, Asko Noormets, Kimberly Novick, Andrew Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. Remote Sensing. 2020; 12 (21):3605.
Chicago/Turabian StyleTsegaye Tadesse; David Hollinger; Yared Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian Wardlow; Gil Bohrer; Kenneth Clark; Ankur Desai; Lianhong Gu; Asko Noormets; Kimberly Novick; Andrew Richardson. 2020. "Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States." Remote Sensing 12, no. 21: 3605.
Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.
Tsegaye Tadesse; David Y. Hollinger; Yared A. Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian D. Wardlow; Gil Bohrer; Kenneth L. Clark; Ankur R. Desai; Lianhong Gu; Asko Noormets; Kimberly A. Novick; Andrew D. Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. 2020, 1 .
AMA StyleTsegaye Tadesse, David Y. Hollinger, Yared A. Bayissa, Mark Svoboda, Brian Fuchs, Beichen Zhang, Getachew Demissie, Brian D. Wardlow, Gil Bohrer, Kenneth L. Clark, Ankur R. Desai, Lianhong Gu, Asko Noormets, Kimberly A. Novick, Andrew D. Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. . 2020; ():1.
Chicago/Turabian StyleTsegaye Tadesse; David Y. Hollinger; Yared A. Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian D. Wardlow; Gil Bohrer; Kenneth L. Clark; Ankur R. Desai; Lianhong Gu; Asko Noormets; Kimberly A. Novick; Andrew D. Richardson. 2020. "Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States." , no. : 1.
The term flash drought is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous U.S. depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past forty years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that flash drought is a composite term that includes several types of event, and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon.
Mahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions. 2020, 2020, 1 -21.
AMA StyleMahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, Martha C. Anderson. Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions. . 2020; 2020 ():1-21.
Chicago/Turabian StyleMahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. 2020. "Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions." 2020, no. : 1-21.
Mahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. Supplementary material to "Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions". 2020, 1 .
AMA StyleMahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, Martha C. Anderson. Supplementary material to "Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions". . 2020; ():1.
Chicago/Turabian StyleMahmoud Osman; Benjamin F. Zaitchik; Hamada S. Badr; Jordan I. Christian; Tsegaye Tadesse; Jason A. Otkin; Martha C. Anderson. 2020. "Supplementary material to "Flash drought onset over the Contiguous United States: Sensitivity of inventories and trends to quantitative definitions"." , no. : 1.
Wildfires are ecosystem‐level drivers of structure and function in many vegetated biomes. While numerous studies have emphasized the benefits of fire to ecosystems, large wildfires have also been associated with the loss of ecosystem services and shifts in vegetation abundance. The size and number of wildfires are increasing across a number of regions, and yet the outcomes of large wildfire on vegetation at large‐scales is still largely unknown. We introduce an exhaustive analysis of wildfire‐scale vegetation response to large wildfires across North America’s grassland biome. We use 18 years of a newly released vegetation dataset combined with 1390 geospatial wildfire perimeters and drought data to detect large‐scale vegetation response among multiple vegetation functional groups. We found no evidence of persistent declines in vegetation driven by wildfire at the biome level. All vegetation functional groups exhibited relatively rapid recovery to pre‐wildfire ranges of variation (ROV) across Great Plains ecoregions, with the exception being a persistent decrease in the abundance of trees in the Northwestern Great Plains. Drought intensity magnified immediate vegetation response to wildfire. Persistent declines in vegetation cover were observed at the scale of single pixels (30‐m), suggesting that these responses were localised and represent extreme cases within larger wildfires. Our findings echo over a century of research demonstrating a biome resilient to wildfire.
Victoria M. Donovan; Dirac Twidwell; Daniel R. Uden; Tsegaye Tadesse; Brian D. Wardlow; Christine H. Bielski; Matthew O. Jones; Brady W. Allred; David E. Naugle; Craig R. Allen. Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome. Earth's Future 2020, 8, 1 .
AMA StyleVictoria M. Donovan, Dirac Twidwell, Daniel R. Uden, Tsegaye Tadesse, Brian D. Wardlow, Christine H. Bielski, Matthew O. Jones, Brady W. Allred, David E. Naugle, Craig R. Allen. Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome. Earth's Future. 2020; 8 (7):1.
Chicago/Turabian StyleVictoria M. Donovan; Dirac Twidwell; Daniel R. Uden; Tsegaye Tadesse; Brian D. Wardlow; Christine H. Bielski; Matthew O. Jones; Brady W. Allred; David E. Naugle; Craig R. Allen. 2020. "Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome." Earth's Future 8, no. 7: 1.
Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coefficient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB.
Getachew Ayehu; Tsegaye Tadesse; Berhan Gessesse. Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia. Remote Sensing 2020, 12, 2138 .
AMA StyleGetachew Ayehu, Tsegaye Tadesse, Berhan Gessesse. Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia. Remote Sensing. 2020; 12 (13):2138.
Chicago/Turabian StyleGetachew Ayehu; Tsegaye Tadesse; Berhan Gessesse. 2020. "Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia." Remote Sensing 12, no. 13: 2138.
The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.
Sneha Kulkarni; Brian Wardlow; Yared Bayissa; Tsegaye Tadesse; Mark Svoboda; Shirishkumar Gedam. Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India. Remote Sensing 2020, 12, 2091 .
AMA StyleSneha Kulkarni, Brian Wardlow, Yared Bayissa, Tsegaye Tadesse, Mark Svoboda, Shirishkumar Gedam. Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India. Remote Sensing. 2020; 12 (13):2091.
Chicago/Turabian StyleSneha Kulkarni; Brian Wardlow; Yared Bayissa; Tsegaye Tadesse; Mark Svoboda; Shirishkumar Gedam. 2020. "Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India." Remote Sensing 12, no. 13: 2091.
The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.
Getachew Ayehu; Tsegaye Tadesse; Berhan Gessesse; Yibeltal Yigrem; Assefa M. Melesse. Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia. Sensors 2020, 20, 1 .
AMA StyleGetachew Ayehu, Tsegaye Tadesse, Berhan Gessesse, Yibeltal Yigrem, Assefa M. Melesse. Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia. Sensors. 2020; 20 (11):1.
Chicago/Turabian StyleGetachew Ayehu; Tsegaye Tadesse; Berhan Gessesse; Yibeltal Yigrem; Assefa M. Melesse. 2020. "Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia." Sensors 20, no. 11: 1.
Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.
Dong-Hyun Yoon; Won-Ho Nam; Hee-Jin Lee; Eun-Mi Hong; Song Feng; Brian D. Wardlow; Tsegaye Tadesse; Mark D. Svoboda; Michael J. Hayes; Dae-Eui Kim. Agricultural Drought Assessment in East Asia Using Satellite-Based Indices. Remote Sensing 2020, 12, 444 .
AMA StyleDong-Hyun Yoon, Won-Ho Nam, Hee-Jin Lee, Eun-Mi Hong, Song Feng, Brian D. Wardlow, Tsegaye Tadesse, Mark D. Svoboda, Michael J. Hayes, Dae-Eui Kim. Agricultural Drought Assessment in East Asia Using Satellite-Based Indices. Remote Sensing. 2020; 12 (3):444.
Chicago/Turabian StyleDong-Hyun Yoon; Won-Ho Nam; Hee-Jin Lee; Eun-Mi Hong; Song Feng; Brian D. Wardlow; Tsegaye Tadesse; Mark D. Svoboda; Michael J. Hayes; Dae-Eui Kim. 2020. "Agricultural Drought Assessment in East Asia Using Satellite-Based Indices." Remote Sensing 12, no. 3: 444.
In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.
Xiang Zhang; Nengcheng Chen; Hao Sheng; Chris Ip; Long Yang; Yiqun Chen; Ziqin Sang; Tsegaye Tadesse; Tania Pei Yee Lim; Abbas Rajabifard; Cristina Bueti; Linglin Zeng; Brian Wardlow; Siqi Wang; Shiyi Tang; Zhang Xiong; Deren Li; Dev Niyogi. Urban drought challenge to 2030 sustainable development goals. Science of The Total Environment 2019, 693, 133536 .
AMA StyleXiang Zhang, Nengcheng Chen, Hao Sheng, Chris Ip, Long Yang, Yiqun Chen, Ziqin Sang, Tsegaye Tadesse, Tania Pei Yee Lim, Abbas Rajabifard, Cristina Bueti, Linglin Zeng, Brian Wardlow, Siqi Wang, Shiyi Tang, Zhang Xiong, Deren Li, Dev Niyogi. Urban drought challenge to 2030 sustainable development goals. Science of The Total Environment. 2019; 693 ():133536.
Chicago/Turabian StyleXiang Zhang; Nengcheng Chen; Hao Sheng; Chris Ip; Long Yang; Yiqun Chen; Ziqin Sang; Tsegaye Tadesse; Tania Pei Yee Lim; Abbas Rajabifard; Cristina Bueti; Linglin Zeng; Brian Wardlow; Siqi Wang; Shiyi Tang; Zhang Xiong; Deren Li; Dev Niyogi. 2019. "Urban drought challenge to 2030 sustainable development goals." Science of The Total Environment 693, no. : 133536.
Objectives: A study was undertaken to assess the prevalence of undernutrition and associated factors among children under five in two drought-prone areas in Ethiopia. Study design and setting: Through a cross-sectional, mixed-methods approach, data were analysed using multistage random sampling methods. Study subjects and outcome measures: Data were collected on socioeconomic factors, demographic characteristics and anthropometric measurements from 350 households. Height-for-age (HAZ), weight-for-height (WHZ) and weight-for-age (WAZ) z-scores of 304 children, aged 0–60 months, were calculated using the WHO Anthro software. Children with z-scores of less than −2 standard deviations (SDs) for HAZ, WHZ and WAZ were classified as stunted, wasted and underweight respectively. Descriptive statistics, t-tests, correlation and regression analyses were used to assess the relationships between independent variables and stunting and underweight. Results: Prevalence of stunting, wasting and underweight were 49.4%, 13.7% and 37.1% respectively. Among independent variables tested, agroecology was significantly associated with stunting (p = 0.012) and underweight (p < 0.001), while livestock holding was significantly correlated with stunting (p = 0.008) and underweight (p = 0.012). Access to irrigation was also significantly associated with stunting (p = 0.028) and underweight (p = 0.016). However, the prevalence of stunting, wasting and underweight was not significantly associated with household size, landholdings or frequency of sickness. Conclusions: The prevalence of undernutrition within the study areas was higher than the national average for Ethiopia. Lowland areas exhibited the highest rates of undernutrition; consequently, interventions that include the enhancement of livestock holdings and access to irrigation should include agroecological factors in an effort to reduce childhood undernutrition.
Shimelis Beyene; Mary S Willis; Martha Mamo; Belaineh Legesse; Teshome Regassa; Tsegaye Tadesse; Yitbarek Wolde-Hawariat; Nur Firyal Roslan. Nutritional status of children aged 0–60 months in two drought-prone areas of Ethiopia. South African Journal of Clinical Nutrition 2019, 33, 152 -157.
AMA StyleShimelis Beyene, Mary S Willis, Martha Mamo, Belaineh Legesse, Teshome Regassa, Tsegaye Tadesse, Yitbarek Wolde-Hawariat, Nur Firyal Roslan. Nutritional status of children aged 0–60 months in two drought-prone areas of Ethiopia. South African Journal of Clinical Nutrition. 2019; 33 (4):152-157.
Chicago/Turabian StyleShimelis Beyene; Mary S Willis; Martha Mamo; Belaineh Legesse; Teshome Regassa; Tsegaye Tadesse; Yitbarek Wolde-Hawariat; Nur Firyal Roslan. 2019. "Nutritional status of children aged 0–60 months in two drought-prone areas of Ethiopia." South African Journal of Clinical Nutrition 33, no. 4: 152-157.
Faisal M. Qamer; Tsegaye Tadesse; Mir Matin; Walter L. Ellenburg; Benjamin Zaitchik. Earth Observation and Climate Services for Food Security and Agricultural Decision Making in South and Southeast Asia. Bulletin of the American Meteorological Society 2019, 100, ES171 -ES174.
AMA StyleFaisal M. Qamer, Tsegaye Tadesse, Mir Matin, Walter L. Ellenburg, Benjamin Zaitchik. Earth Observation and Climate Services for Food Security and Agricultural Decision Making in South and Southeast Asia. Bulletin of the American Meteorological Society. 2019; 100 (6):ES171-ES174.
Chicago/Turabian StyleFaisal M. Qamer; Tsegaye Tadesse; Mir Matin; Walter L. Ellenburg; Benjamin Zaitchik. 2019. "Earth Observation and Climate Services for Food Security and Agricultural Decision Making in South and Southeast Asia." Bulletin of the American Meteorological Society 100, no. 6: ES171-ES174.
Drought is a major natural hazard with impacts across many sectors in a society. The main goal of this study was to link users’ and decision makers’ requirements with scientific information for effective use of remotely sensed data in a data-scarce region. The specific objectives are to: 1) present preliminary results from drought forecast tool evaluations addressing users’ requirements in the study region, and 2) present the participatory research approach followed, with gaps and challenges identified. In this research, we used a participatory system design methodology that uses dialogue between managers and scientists on how to enhance the use of models’ outputs and prediction products and improve the delivery of this information to decision makers. In this participatory research approach, it was confirmed that the major barriers for the use of drought model products were lack of skill, lack of understanding, and lack of trust of the products. These information use barriers can be addressed by targeted decision makers’ skill development training and use of the available public media for increasing the awareness level on the availability and benefits of drought forecast products. Future research may focus on data dissemination and concept implementation as well as data management and information usability (i.e., addressing users’ requirements).
Getachew B. Demisse; Tsegaye Tadesse; Nicole Wall; Tonya Haigh; Yared Bayissa; Andualem Shiferaw. Linking seasonal drought product information to decision makers in a data-sparse region: A case study in the Greater Horn of Africa. Remote Sensing Applications: Society and Environment 2019, 14, 200 -206.
AMA StyleGetachew B. Demisse, Tsegaye Tadesse, Nicole Wall, Tonya Haigh, Yared Bayissa, Andualem Shiferaw. Linking seasonal drought product information to decision makers in a data-sparse region: A case study in the Greater Horn of Africa. Remote Sensing Applications: Society and Environment. 2019; 14 ():200-206.
Chicago/Turabian StyleGetachew B. Demisse; Tsegaye Tadesse; Nicole Wall; Tonya Haigh; Yared Bayissa; Andualem Shiferaw. 2019. "Linking seasonal drought product information to decision makers in a data-sparse region: A case study in the Greater Horn of Africa." Remote Sensing Applications: Society and Environment 14, no. : 200-206.
To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt” (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 > 0.8) for one-month prediction and a relatively lower value (R2 ≅ 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin.
Yared Bayissa; Tsegaye Tadesse; Getachew Demisse. Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia. Remote Sensing 2019, 11, 371 .
AMA StyleYared Bayissa, Tsegaye Tadesse, Getachew Demisse. Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia. Remote Sensing. 2019; 11 (4):371.
Chicago/Turabian StyleYared Bayissa; Tsegaye Tadesse; Getachew Demisse. 2019. "Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia." Remote Sensing 11, no. 4: 371.
In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.
Getachew Ayehu; Tsegaye Tadesse; Berhan Gessesse; Yibeltal Yigrem. Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia. Remote Sensing 2019, 11, 125 .
AMA StyleGetachew Ayehu, Tsegaye Tadesse, Berhan Gessesse, Yibeltal Yigrem. Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia. Remote Sensing. 2019; 11 (2):125.
Chicago/Turabian StyleGetachew Ayehu; Tsegaye Tadesse; Berhan Gessesse; Yibeltal Yigrem. 2019. "Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia." Remote Sensing 11, no. 2: 125.
Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000-2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead-times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices, derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean R2cv values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.
Francisco Zambrano; Anton Vrieling; Andy Nelson; Michele Meroni; Tsegaye Tadesse. Prediction of agricultural drought in Chile from multiple spatio-temporal data sources. 2019, 1 .
AMA StyleFrancisco Zambrano, Anton Vrieling, Andy Nelson, Michele Meroni, Tsegaye Tadesse. Prediction of agricultural drought in Chile from multiple spatio-temporal data sources. . 2019; ():1.
Chicago/Turabian StyleFrancisco Zambrano; Anton Vrieling; Andy Nelson; Michele Meroni; Tsegaye Tadesse. 2019. "Prediction of agricultural drought in Chile from multiple spatio-temporal data sources." , no. : 1.